NDLI: Generalized predictive control for π-type rudder anti-pitching based on nonlinear adaptive intelligent model

Content Provider	IEEE Xplore Digital Library
Author	Hongli Chen Ziyuan Wang Zhongli Ma
Copyright Year	2014
Description	Author affiliation: Coll. of Autom., Harbin Eng. Univ., Harbin, China (Hongli Chen; Ziyuan Wang; Zhongli Ma)
Abstract	For π-type rudder anti-pitching system, an iterative predictive control algorithm based on information fusion is proposed. Optimal estimation of co-state sequence and control sequence is obtained by the fusion of future reference trajectory in quadratic performance index function and soft constraint information together with hard constraint information in system function. By taking 442T surface ship as the research object, typical sea state hydrodynamic coefficients are obtained according to pool experiment results and strip theory, and an adaptive three-dimension nonlinear intelligence model is built according to real-time changing speed, sea situations and course angle. The Radial Basis Function Neural Network (RBF) is optimized by optimal stopping rule avoiding over learning. Finally, simulations on information fusion predictive control were conducted on the proposed adaptive intelligence model with sea colored noise. The results show that the proposed algorithm has good efficiency in adaptively controlling on ship's longitudinal motion with all weather.
Starting Page	1470
Ending Page	1474
File Size	388302
Page Count	5
File Format	PDF
ISBN	9781479939787
e-ISBN	9781479939794
DOI	10.1109/ICMA.2014.6885916
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Marine vehicles Adaptation models Mathematical model Hydrodynamics Predictive control Training Estimation Generalized Predictive Control π-type rudder anti-pitching hydrodynamic coefficients nonlinear adaptive intelligent model
Content Type	Text
Resource Type	Article

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